Sr. Manager, Enterprise Data & Analytics

About Us:

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation.

Its APA system, built on the industry's first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics-all delivered with enterprise-grade security and governance.

Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

Our opportunity:

We are seeking a Senior Manager to lead the delivery of scalable enterprise data, analytics, and AI solutions through team leadership, technical direction, and business partnership. This role will translate business priorities into a data and analytics roadmap, guide the architecture of the enterprise data warehouse (EDW) and data lake ecosystem, and oversee the delivery of trusted data products and reliable ingestion and transformation pipelines. As the primary IT partner for business stakeholders, this individual will align priorities, communicate progress and trade-offs, and drive adoption and measurable business value. The role will provide technical oversight and targeted hands-on guidance across Informatica for data ingestion and integration, Google BigQuery for cloud data warehousing and data lake capabilities, and Tableau for analytics and visualization.

Who you'll report to:

This role reports to our VP, Business Applications

Location:

Hybrid role with regular onsite workdays in our San Jose, CA office required. Other U.S locations may be considered.

You will make an impact by being responsible for:

• People leadership: Lead and develop a team of data engineering, analytics, and architecture professionals. Hire and onboard talent, set clear goals and ownership, delegate effectively, provide regular coaching and feedback, and build team capability through development planning and succession readiness
• Solution architecture: Own the target-state EDW, data lake, and analytics architecture and guide its evolution through design reviews and clear decision-making. Direct and review logical and physical data models, integration patterns, security controls, metadata, lineage, and non-functional requirements, ensuring designs meet business priorities and enterprise standards
• Roadmap and resource planning: Own the enterprise data and analytics delivery roadmap and backlog, prioritize initiatives with business stakeholders, and align commitments with team capacity and dependencies. Develop resource and investment recommendations, manage delivery within approved allocations, and review platform consumption, licensing, and operating costs with the relevant owners
• Data ingestion and integration: Lead the team's design and delivery of batch and near-real-time Informatica pipelines that ingest data from enterprise applications, databases, APIs, files, and third-party platforms. Establish reusable integration patterns, review critical designs, and ensure delivery meets agreed reliability and data-freshness requirements
• BigQuery engineering: Direct the team's development and optimization of curated data layers, dimensional models, data marts, and lakehouse-style datasets in Google BigQuery. Set and review engineering standards for partitioning and clustering, and make informed trade-offs across performance, scalability, and cost
• Data transformation: Establish reusable transformation standards and oversee the team's implementation of data standardization, reconciliation, and orchestration across raw, trusted, and consumption layers. Review high-risk changes and ensure transformation logic is tested, documented, and maintainable
• Analytics and visualization: Partner with business leaders to agree KPI definitions, prioritize analytical needs, and direct the delivery of governed Tableau dashboards, reports, and self-service analytics experiences. Align business and technical owners on acceptance criteria and review adoption, usability, and contribution to business decision-making
• Business analysis: Elicit and document requirements, profile source data, perform exploratory analysis, identify trends and anomalies, and translate findings into actionable recommendations
• Data quality and governance: Establish data quality rules, monitoring, exception handling, lineage, reference data, business definitions, stewardship processes, and auditability
• Security and compliance: Apply role-based access, least-privilege principles, sensitive-data protections, retention requirements, and applicable privacy and regulatory controls
• AI solutions: Lead the prioritization, design, and delivery of AI-enabled solutions that securely use governed EDW and data lake assets. Guide technical teams in implementing Role-Based Access Control (RBAC) and Row-Level Access (RLA) so users, applications, and AI services access only authorized data, and oversee solution evaluation, pro